Previous work mainly focuses on the single imbalance case or grouping several settings into one task, offering limited insights into the effects of CI on FSL and making it challenging to quantify its effects [1]}, [2]}, [3]}, [4]}. A common approach to mitigate imbalance is Random-Shot meta-training [2]}, which exposes the model to imbalanced tasks during meta-training. However, previous work provides little insight into the effectiveness of this procedure on the imbalanced FSL evaluation task. Furthermore, minimal work exists that investigates meta-training outcomes under an imbalanced distribution of classes at the meta-dataset level, while this case is common in recent FSL applications [6]}, [1]} and meta-learning benchmarks [2]}. The CI problem is well-known within the supervised learning community, which has systematically produced strategies to deal with the problem, such as the popular Random Over-Sampling [9]} that aims at rebalancing minority classes by uniform sampling. While such strategies have been extensively studied on many supervised learning problems, there is little understanding of how they behave with the recently proposed FSL methods in the low-data regime.
